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There is increasing interest in using virtual reality (VR) in robotic neurorehabilitation. However, the use of conventional VR displays (i.e., computer screens), implies several transformations between the real movements in 3D and their 2D virtual representations that might negatively impact the rehabilitation interventions. In this study, we compared the impact on movement quality and cognitive load of novel vs. standard visualization technologies: i) Immersive VR (IVR) head-mounted display (HMD), ii) Augmented reality (AR) HMD, and iii) Computer screen. Twenty healthy participants performed simultaneously a motor and a cognitive task. Goal-oriented reaching movements were recorded using an HTC Vive controller. The cognitive load was assessed by the accuracy on a simultaneous counting task. The movement quality improved when visualizing the movements in IVR, compared to the computer screen. These improvements were more evident for locations that required movements in several dimensions. We found a trend to higher movement quality in AR than Screen, but worse than IVR. No significant difference was observed between modalities for the cognitive load. These results provide encouraging evidence that VR interventions using HMDs might be more suited for reaching tasks in several dimensions than a computer screen. Technical limitations might still limit the beneficial effects of AR, both in movement quality and cognitive load.

Frontiers in Robotics and AI, 6, ISSN:2296-9144

BACKGROUND Aphasia is the loss or impairment of language functions and affects every day social life. The disorder leads to the inability to understand and be understood in both written and verbal communication and affects the linguistic modalities of auditory comprehension, verbal expression, reading, and writing. Due to heterogeneity of the impairment, the training must be adapted individually and dynamically to the patient needs. An important factor for a successful aphasia therapy is dose frequency. Computer-based applications allow patients to train independently with high intensity at home. But most of the newest tele-rehabilitation applications focus on one linguistic modality, whereas there is currently a lack of validated tele-rehabilitation systems that focus on multimodal linguistic modalities. OBJECTIVE The aim of this project was to develop a multimodal system that enables aphasic patients to train at home using language-related tasks autonomously and also allows therapists to remotely assign new tasks and track the patient’s progress as well as to create new individual exercises. METHODS The system consists of two main parts. First, the patient’s interface, which allows the patient to exercise. Second, the therapist’s interface, which allows the therapist to allocate new exercises to the patient and supervise the progress of the patient. The system has been evaluated by therapists and patients by validated questionnaires in terms of usability and motivation. RESULTS A total of 11 speech and language therapists (age, MD = 28, SD =7) and 15 patients (age, MD = 53, SD =10) with diagnosed aphasia participated in this study. Patients rated the Bern Aphasia App in terms of usability (scale zero to hundred) as excellent (score above 70, 95% CI 85.9 to 100.0, t (14) = 2.16, P = .024) and therapists as good (score above 85, 95% CI 70.5 to 100.0, t (10) = 2.21, P = .026). Furthermore, patients highly enjoyed (scale zero to six) solving the exercises (score above 3, 95% CI 3.1 to 6.0, t (4) = 2.58, P = .031). CONCLUSIONS Based on the questionnaire scores, the developed system is well accepted and simple to use by patients and therapists. Furthermore, the new system allows patients with different types of aphasia to train with a high intensity independently at home. Thus, the novel system has potential for treatment of aphasic patients as a supplement to face-to-face therapy.

There is increasing interest in using robotic devices to provide rehabilitation therapy following neurological injuries. During robotic gait training, patients are assisted with body-weight support and provided with physical guidance from a robotic device to move their legs into a correct gait pattern. Gait rehabilitation robots allow for longer training duration, promote more movement repetitions, improve patient safety and motivation, reduce the therapists' burden, and eventually improve the therapeutic outcome. This chapter introduces the rationale of robot-assisted gait training and follows with an overview of existing gait rehabilitation robots and their control strategies. It finishes with a summary of existing clinical trials, which showed that training with robotic rehabilitation devices is, at least, as effective as conventional physiotherapy. It concludes with the recommendation that further clinical studies are required in order to define the most appropriate robotic technical features based on the type of rehabilitation tasks, patients' neurological injuries, and their stage of recovery.

Research on motor learning suggests that training with haptic guidance enhances learning of the timing components of motor tasks, whereas error amplification is better for learning the spatial components. We present a novel mixed guidance controller that combines haptic guidance and error amplification to simultaneously promote learning of the timing and spatial components of complex motor tasks. The controller is realized using a force field around the desired position. This force field has a stable manifold tangential to the trajectory that guides subjects in velocity-related aspects. The force field has an unstable manifold perpendicular to the trajectory, which amplifies the perpendicular (spatial) error. We also designed a controller that applies randomly varying, unpredictable disturbing forces to enhance the subjects’ active participation by pushing them away from their “comfort zone.” We conducted an experiment with thirty-two healthy subjects to evaluate the impact of four different training strategies on motor skill learning and self-reported motivation: (i) No haptics, (ii) mixed guidance, (iii) perpendicular error amplification and tangential haptic guidance provided in sequential order, and (iv) randomly varying disturbing forces. Subjects trained two motor tasks using ARMin IV, a robotic exoskeleton for upper limb rehabilitation: follow circles with an ellipsoidal speed profile, and move along a 3D line following a complex speed profile. Mixed guidance showed no detectable learning advantages over the other groups. Results suggest that the effectiveness of the training strategies depends on the subjects’ initial skill level. Mixed guidance seemed to benefit subjects who performed the circle task with smaller errors during baseline (i.e., initially more skilled subjects), while training with no haptics was more beneficial for subjects who created larger errors (i.e., less skilled subjects). Therefore, perhaps the high functional difficulty of the tasks limited the potential benefit of mixed guidance. Adding random disturbing forces during training reduced the learning effect size compared to no haptics. The unanticipated forces also decreased the subjects’ feelings of competence while did not increase their effort and interest. Further studies with mildly affected neurologically patients employing easier tasks need to be performed in order to evaluate the applicability of our approaches in rehabilitation., Frontiers in Robotics and AI, 4, ISSN:2296-9144

Previous results suggest that haptic guidance enhances learning of the timing components of motor tasks, whereas error amplification is better for learning the spatial components. In this paper we evaluate a novel mixed guidance controller that combines haptic guidance and error amplification to simultaneously promote learning of the timing and spatial components. The controller is realized using a saddle-like force field around the desired movement position. This force field has a stable manifold tangential to the trajectory that guides subjects in velocity related aspects. The force field has its unstable manifold perpendicular to the trajectory, which amplifies the normal (spatial) error. We conducted an experiment with twenty nine healthy subjects to test whether training with the mixed guidance controller resulted in better learning than training without guidance or with guidance-as-needed. Subjects trained two tasks: a continuous rhythmic task (circle) and a continuous single task (line). We found that the effectiveness of the training strategy depended on the task. Training with mixed guidance was especially beneficial for learning the timing components of the line, but limited learning of the circle. Perhaps the continuous change in the force directions during training of the circle was too difficult to interpret.

Robot-aided gait training has been presented as a promising technique to improve rehabilitation in patients with neurological lesions. Although robotic guidance is often used to reduce performance errors while practicing, there is currently little evidence that robotic guidance is more beneficial for human motor learning than unassisted practice. Research on motor learning has emphasized that movement errors drive motor adaptation. Thereby, robotic algorithms that augment errors rather than decrease them have a great potential to provoke better motor learning. In this paper, we present a novel control algorithm that modulates movement errors by limiting dangerous and discouraging large errors with haptic guidance, while augmenting awareness of task relevant errors by means of error amplification. We also designed a controller that applies random disturbance torques that can work on top of the error-modulating controller. The controllers were evaluated using robotic testing. The error-modulating controller resulted in larger errors due to error amplification, but limited the maximum deviation from the desired pattern, thanks to haptic guidance. Adding random disturbance torques increased gait variability. The combination of the random disturbance and error-modulating controllers increased the kinematic errors and gait variability, while limited large errors, providing an excellent framework to enhance motor learning.

To date, the neurophysiological correlates of muscle activation required for weight bearing during walking are poorly understood although, a supraspinal involvement has been discussed in the literature for many years. The present study investigates the effect of simulated ground reaction forces (0, 20, and 40 % of individual body weight) on brain activation in sixteen healthy participants. A magnetic resonance compatible robot was applied to render three different levels of load against the feet of the participants during active and passive gait-like stepping movements. Brain activation was analyzed by the means of voxel-wise whole brain analysis as well as by a region-of-interest analysis. A significant modulation of brain activation in sensorimotor areas by the load level could neither be demonstrated during active nor during passive stepping. These observations suggest that the regulation of muscle activation under different weight-bearing conditions during stepping occurs at the level of spinal circuitry or the brainstem rather than at the supraspinal level.

Berichte aus der Sportwissenschaft, Human Movement and Technology : book of abstracts - 11th Joint Conference on Motor Control & Learning, Biomechanics & Training, ISBN:978-3-8440-4707-3, ISBN:3-8440-4707-7

Individuals with severe neurologic injuries often cannot participate in robotic rehabilitation because they do not retain sufficient residual motor control to initiate the robotic assistance. In these situations, brain- and body-computer interfaces have emerged as promising solutions to control robotic devices. In a previous experiment conducted with healthy subjects, we showed that detecting motor execution accurately was possible using only the autonomic nervous system (ANS) response. In this paper, we investigate the feasibility of such a body-machine interface to detect motion intention by monitoring the ANS response in stroke survivors. Four physiological signals were measured (blood pressure, breathing rate, skin conductance response and heart rate) while participants executed and imagined a grasping task with their impaired hand. The physiological signals were then used to train a classifier based on hidden Markov models. We performed an experiment with four chronic stroke survivors to test the effectiveness of the classifier to detect rest, motor execution and motor imagery periods. We found that motion execution can be accurately classified based only on peripheral autonomic signals with an accuracy of 72.4%. The accuracy of classifying motion imagery was 62.4%. Therefore, attempting to move was a more effective strategy than imagining the movement. These results are encouraging to perform further research on the use of the ANS response in body-machine interfaces.

Experimental Brain Research, 231 (3), ISSN:0014-4819, ISSN:1432-1106

Bouncing a ball with a racket is a hybrid rhythmic-discrete motor task, combining continuous rhythmic racket movements with discrete impact events. Rhythmicity is exceptionally important in motor learning, because it underlies fundamental movements such as walking. Studies suggested that rhythmic and discrete movements are governed by different control mechanisms at different levels of the Central Nervous System. The aim of this study is to evaluate the effect of fixed/fading haptic guidance on learning to bounce a ball to a desired apex in virtual reality with varying gravity. Changing gravity changes dominance of rhythmic versus discrete control: The higher the value of gravity, the more rhythmic the task; lower values reduce the bouncing frequency and increase dwell times, eventually leading to a repetitive discrete task that requires initiation and termination, resembling target-oriented reaching. Although motor learning in the ball-bouncing task with varying gravity has been studied, the effect of haptic guidance on learning such a hybrid rhythmic-discrete motor task has not been addressed. We performed an experiment with thirty healthy subjects and found that the most effective training condition depended on the degree of rhythmicity: Haptic guidance seems to hamper learning of continuous rhythmic tasks, but it seems to promote learning for repetitive tasks that resemble discrete movements.

There is an initial body of work that compared the effectiveness of robotic strategies that amplify or reduce movement errors on motor learning. However, these comparative studies still show inconclusive results, probably because they searched for the robotic strategy that enhances learning, independently of the subjects' skill level and the specific characteristics of the task to be learned. Some theories have suggested that optimal learning is achieved when the difficulty of the task is appropriate for the individual subject's level of expertise. Additionally, the specific characteristics of the task to be learned (e.g. the task's timing component) might play an important role on the effectiveness of different training strategies. In this paper, we overview the research performed in the Sensory-Motor System laboratory at ETH Zurich that aims to find the robotic strategies that could enhance motor learning based on the participants' skill level and specific characteristics of the task to be learned. We performed several motor learning experiments and found that haptic guidance seems to be particularly helpful for initially less skilled subjects, while error amplification is more beneficial for skilled subjects. The rhythmicity and duration of the movement also seem to be key factors to consider when selecting the robotic training strategies that enhance motor learning. The impacts of these training strategies on neurological patients need further investigations.

Background Robotic haptic guidance is the most commonly used robotic training strategy to reduce performance errors while training. However, research on motor learning has emphasized that errors are a fundamental neural signal that drive motor adaptation. Thus, researchers have proposed robotic therapy algorithms that amplify movement errors rather than decrease them. However, to date, no study has analyzed with precision which training strategy is the most appropriate to learn an especially simple task. Methods In this study, the impact of robotic training strategies that amplify or reduce errors on muscle activation and motor learning of a simple locomotor task was investigated in twenty two healthy subjects. The experiment was conducted with the MAgnetic Resonance COmpatible Stepper (MARCOS) a special robotic device developed for investigations in the MR scanner. The robot moved the dominant leg passively and the subject was requested to actively synchronize the non-dominant leg to achieve an alternating stepping-like movement. Learning with four different training strategies that reduce or amplify errors was evaluated: (i) Haptic guidance: errors were eliminated by passively moving the limbs, (ii) No guidance: no robot disturbances were presented, (iii) Error amplification: existing errors were amplified with repulsive forces, (iv) Noise disturbance: errors were evoked intentionally with a randomly-varying force disturbance on top of the no guidance strategy. Additionally, the activation of four lower limb muscles was measured by the means of surface electromyography (EMG). Results Strategies that reduce or do not amplify errors limit muscle activation during training and result in poor learning gains. Adding random disturbing forces during training seems to increase attention, and therefore improve motor learning. Error amplification seems to be the most suitable strategy for initially less skilled subjects, perhaps because subjects could better detect their errors and correct them. Conclusions Error strategies have a great potential to evoke higher muscle activation and provoke better motor learning of simple tasks. Neuroimaging evaluation of brain regions involved in learning can provide valuable information on observed behavioral outcomes related to learning processes. The impacts of these strategies on neurological patients need further investigations.

Background Brain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients. Methods Seven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxy- and deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and biosignals combined. Results fNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062). Conclusions This study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired biosignals. Combining fNIRS data with biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject’s hemodynamic and physiological state., Journal of NeuroEngineering and Rehabilitation, 10, ISSN:1743-0003

SUMMARYHuman reaction to external stimuli can be investigated in a comprehensive way by using a versatile virtual-reality setup involving multiple display technologies. It is apparent that versatility remains a main challenge when human reactions are examined through the use of haptic interfaces as the interfaces must be able to cope with the entire range of diverse movements and forces/torques a human subject produces. To address the versatility challenge, we have developed a large-scale reconfigurable tendon-based haptic interface which can be adapted to a large variety of task dynamics and is integrated into a Cave Automatic Virtual Environment (CAVE). To prove the versatility of the haptic interface, two tasks, incorporating once the force and once the velocity extrema of a human subject's extremities, were implemented: a simulator with 3-DOF highly dynamic force feedback and a 3-DOF setup optimized to perform dynamic movements. In addition, a 6-DOF platform capable of lifting a human subject off the ground was realized. For these three applications, a position controller was implemented, adapted to each task, and tested. In the controller tests with highly different, task-specific trajectories, the three robot configurations fulfilled the demands on the application-specific accuracy which illustrates and confirms the versatility of the developed haptic interface.

Little is known about the neural correlates underlying the control of highly coordinated bilateral lower limb movements. In this paper we present an fMRI paradigm consisting of balancing a virtual ball rolling on a virtual bar. The subject can control the position of the bar by its lower limbs using the MR-compatible stepper MARCOS. Concurrent visual and force feedback about the state of the physical model of ball and bar is presented to the subject during task execution. In a pilot study, significant cerebellar and parieto-frontal activity was found, according to physiological expectations, demonstrating the feasibility of the experimental set-up. Further experiments will help to better understand human motor control as well as to develop more targeted rehabilitation strategies for patients with neurological movement disorders of the lower limbs.

Background Brain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients. Methods Seven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxy- and deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and biosignals combined. Results fNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062). Conclusions This study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired biosignals. Combining fNIRS data with biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject’s hemodynamic and physiological state.

While robotic guidance can improve ongoing performance of a motor task, several studies have found that it ultimately impairs motor learning. However, some recent studies found that the haptic demonstration of optimal timing, rather than movement magnitude, apparently transfers to participants trained with robotic guidance. Here, we describe the synthesis, design and control of a novel tendon-based haptic device able to apply different forms of physical guidance to train a task for which timing is the critical performance determinant: a forearm tennis stroke. We designed three guidance control strategies: a position controller to apply a fixed haptic guidance, a path controller with zero-force controller inside a safety tunnel around the desired trajectory to apply no guidance, and a guidance-as-needed controller to adapt the amount of support as training progresses. The controllers were experimentally tested and evaluation results are presented. The position controller was able to track the desired trajectory, with minimum errors. The path controller avoided leaving the tunnel, but the zero-force controller inside the tunnel allowed subjects to intend the task by themselves. Finally, the guidance-as-needed controller was able to modulate the guidance, allowing more autonomy to initiate the movements as training progressed, while limiting tracking and velocity errors.

Little is known about the neural correlates underlying the control of highly coordinated bilateral lower limb movements. In this paper we present an fMRI paradigm consisting of balancing a virtual ball rolling on a virtual bar. The subject can control the position of the bar by its lower limbs using the MR-compatible stepper MARCOS. Concurrent visual and force feedback about the state of the physical model of ball and bar is presented to the subject during task execution. In a pilot study, significant cerebellar and parieto-frontal activity was found, according to physiological expectations, demonstrating the feasibility of the experimental set-up. Further experiments will help to better understand human motor control as well as to develop more targeted rehabilitation strategies for patients with neurological movement disorders of the lower limbs.

This paper presents the concept and initial results of a novel approach for robot assisted sensorimotor training in stroke rehabilitation. It is based on a brain-body-robot interface (B 2RI), combining both neural and physiological recordings, that detects the intention to perform a motor task. By directly including the injured brain into the therapy, we ultimately aim at providing a new method for severely impaired patients to engage in active movement therapy. In the present study, seven healthy subjects performed an isometric finger pinching task while functional near-infrared spectroscopy (fNIRS) signals from motor cortical areas and biosignals were recorded simultaneously. Results showed an insignificant increase in the blood pressure during the preparation period prior to motor execution. During the execution period, significant changes in oxy-and deoxyhemoglobin were found in the primary motor cortex, accompanied by an increase in blood pressure, respiration rate and galvanic skin response (GSR). Cortical measurements of premotor areas and heart rate revealed significant changes at the subject level with large inter-subject variability. The results presented here will serve as priors for the design of further studies to test the efficacy of the concept with stroke patients, and the found effects will provide a basis for the development of a classifier for a future B 2RI. © 2011 IEEE.

The goal of robotic therapy is to provoke motor plasticity via the application of robotic training strategies. Although robotic haptic guidance is the commonly used motor-training strategy to reduce performance errors while training, research on motor learning has emphasized that errors are a fundamental neural signal that drives motor adaptation. Thus, researchers have proposed robotic therapy algorithms that amplify movement errors rather than decrease them. Studying the particular brain regions involved in learning under different training strategies might help tailoring motor training conditions to the anatomical location of a focal brain insult. In this paper, we evaluate the brain regions involved in locomotion adaptation when training with three different conditions: without robotic guidance, with a random-varying force disturbance, and with repulsive forces proportional to errors. We performed an fMRI pilot study with four healthy subjects who stepped in an fMRI compatible walking robotic device. Subjects were instructed to actively synchronize their left leg with respect to their right leg (passively guided by the robot) while their left leg was affected by any of the three conditions. We observed activation in areas known to be involved in error processing. Although we found that all conditions required the similar cortical network to fulfill the task, we observed a tendency towards more activity in the motor/sensory network as more "challenged" the subjects were.